This IEEE 1484.20.1 standard defines a data model for describing, referencing, and sharing competency definitions. It is quite simple and in this blog entry I want to explain only the difficult to understand elements. Below is a simplified graphical data model of RCD created in form of a diagram:

Arrows denote element that are part of an data structure. Red nodes denote a mandatory information, blue nodes represent an optional data.

The most interesting part of the specification is the definition information. The definition is an optional information. There can exist several definitions in parallel. A definition can be stated in different ways.

• Either there exists already a definition, so I reference it by the source element.
• Or I make one or more statements about the RCD in the definition section.

The statement element itself may be made in different ways. Note, that those alternatives are conditionally optional,i.e., only one of the alternatives is to be used. Alternatives are:

• Statement identifier is a unique label within the scope of the definition. I do not understand wether this information is a label for the statement itself or if it refrences some other statement. If it is a unique reference label, where is it referenced then? If it is a reference, where is the unique reference label stored? The fact that this element is conditionally optional it suggests that the element is used for referencing other statements.
• Statement name is just a string name. Examples are: Condition, Action, Standard, Outcome, Criteria, etc.
• Statement text is some more verbal text that describes the competency.
• Statement token is a data structure that includes a pointer to some location which defines a vocabulary to use and a value chosen from this vocabulary.

Example. I have to add one. So far I came across an example shipped together with IMS Rubric /e-portfolio specification, but I do not like it very much. At least, it shows you how to use the definition source model element and some statements. Surprisingly, IMS uses for statements both: statement name and text. This breaks the conditionally optional concept.

The example basically defines the following:

• It references a basic rubric modelin the model source field
• It makes three statements:
  1. Regarding measure,  all artifacts and work samples are clearly and directly related to one or more national, regional, or state teaching standards and provide evidence of professional practice.
  2. Regarding score, a value of 9 has been achieved.
  3. According to skill-level, the token exemplary has been chosen from the vocabulary source at URN:FICTIONAL:UWSTOUT:SKILL-LEVELS.

Well, need to add more good examples including one in IMS RCDEO XML specification.

Metadata is an optional information. According to the specification, there is no restriction, but I guess that it is recommended to stick to the LOM standard. Also, it is suggested to put here the information about the schema and its version the information model sticks to.

By the way, what is the difference between data model and information model? The IEEE draft presents a data model while IMS is talking about an information model. Have to check this.

The master thesis task mentioned in an earlier blog entry on competency matching has been cancelled. I will implement it myself. I plan to start with with several steps:

• Check for related work.
• Check for competency models.
• Check for technologies to be used.

Check for related work. There has been some work on competency matching such as Kowien and Professional-Learning. Kowien is a project that ran out in 2004, and as far as I know, it has not been continued. However, it investigated how to use ontologies for the management of competency profiles.

Professional Learning is a project that starts where Kowien finished. It also favours a solution based on semantic web technologies. The goal was to propose a framwork description that glues together several components which interrelate with each other in a business / on-the-job setting where being up to date educationally is a requirement. The componets are: user profile, HR system, learning repository, and job tasks description. The components are glued by competencies.  An individual has or lacks competencies; job tasks are described by required competencies; learning objects goals can be decribed in form of competencies. HR Systems idenfy competency gaps and recommend learning objects to close competency gaps. 

Check for competency models. Claude Ostyn lists a few comptency models standards. Among others he lists the IEEE RCD data standard for reusable competency definitions which has been implemented by IMS in IMS RCDEO in an information model. Right now, Ostyn is working on on a new standard data definition, Simple Reusable Competency Map (SRCM) which aims “to be used for describing, referencing, and exchanging data about the relationships between competencies, primarily in the context of online and distributed learning”. The basic idea is to concatenate RCDs in a in direct acyclic graph. The description of data about relationships between competencies is a bit misleading, as the parent-children graph relation implies a semantic consistsOf/isPartOf relationship. I am not quite sure whether the standard is also open for the description of other relationships.  I have to investigate this.

Another short but nice competeny model (in this case ontology)  is presented in the professional learning. The basic idea is to describe competencies in a hierarchical manner including a description of the competency level (or even range). A subsumes and isComposedOf relationship helps to use competency substitutions and competency subsumptions to deduce correct matchings.

Both models, SRCM and Professional Learning, aim to connect job/task/learning object descriptions with an individual’s profile. The glueing stuff are the competency descriptions.

Check for technologies to be used. A possible and my favourite solution to my task is a semantic web approach. The reasons are:

• A clear distinction beween model and logic.
• Different ontologies depending on the cotext can be devised for different purposes while the basic ontology model is used.
• Depending on the  ontology language (inference) some functionalities are provided for free, e.g. class subsumption or transitivity (OWL).
• There already exist several very good frameworks that additioanlly offer query possiblitie, another abstraction level. That is we have a deductive modelling language, a set of possible inference engines, and a deductive query language.

I reviewed several open source framworks: Jena, Sesame, and IBM Semantic Layer Research Plattform. While Jena has been there for a while it is considered to be slow and too inflexible. I do not want to comment this as I am not an expert. Collegues at DFKI recommended to use Sesame. One of the main benefits is its  abstraction to the inference engine. Through a plugin mechanism it is possible to include own inference implementations. Sesame itself comes with RDF and RDFS inference. However, OntoText offers  an OWL inference plugin, called Owlim. Finally, IBMs Semantic Layer Research PLattform is an application plattform for applications that need semantic data store and inference layer. In the recent version, the decision was made to change from Jena to Sesame.

Due to my collected data and because I have to make a decision very soon, my choice is in favour of Sesame. I’ll see how far I get with that.